Skip to main content
SpringerLink
Log in

Domain Adaptation for Anomaly Detection on Heterogeneous Graphs in E-Commerce

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13981))

Included in the following conference series:

  • 1543 Accesses

Abstract

Anomaly detection models have been the indispensable infrastructure of e-commerce platforms. However, existing anomaly detection models on e-commerce platforms face the challenges of “cold-start” and heterogeneous graphs which contain multiple types of nodes and edges. The scarcity of labeled anomalous training samples on heterogeneous graphs hinders the training of reliable models for anomaly detection. Although recent work has made great efforts on using domain adaptation to share knowledge between similar domains, none of them considers the problem of domain adaptation between heterogeneous graphs. To this end, we propose a Domain Adaptation method for heterogeneous GRaph Anomaly Detection in E-commerce (DAGrade). Specifically, DAGrade is designed as a domain adaptation approach to transfer our knowledge of anomalous patterns from label-rich source domains to target domains without labels. We apply a heterogeneous graph attention neural network to model complex heterogeneous graphs collected from e-commerce platforms and use an adversarial training strategy to ensure that the generated node vectors of each domain lay in the common vector space. Experiments on real-life datasets show that our method is capable of transferring knowledge across different domains and achieves satisfactory results for online deployment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.taobao.com.

  2. 2.

    https://www.lazada.com.

References

  1. Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. 68, 90–113 (2016)

    Article  Google Scholar 

  2. Ahmed, M., Mahmood, A.N., Islam, M.R.: A survey of anomaly detection techniques in financial domain. Futur. Gener. Comput. Syst. 55, 278–288 (2016)

    Article  Google Scholar 

  3. Al-Stouhi, S., Reddy, C.K.: Transfer learning for class imbalance problems with inadequate data. Knowl. Inf. Syst. 48(1), 201–228 (2016)

    Article  Google Scholar 

  4. Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)

  5. Cai, Y., Zhu, D.: Fraud detections for online businesses: a perspective from blockchain technology. Financial Innov. 2(1), 1–10 (2016). https://doi.org/10.1186/s40854-016-0039-4

    Article  Google Scholar 

  6. Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., Tang, J.: Representation learning for attributed multiplex heterogeneous network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1358–1368 (2019)

    Google Scholar 

  7. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844–3852 (2016)

    Google Scholar 

  8. Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144 (2017)

    Google Scholar 

  9. Fu, T.Y., Lee, W.C., Lei, Z.: HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1797–1806 (2017)

    Google Scholar 

  10. Fujita, H., Matsukawa, T., Suzuki, E.: One-class selective transfer machine for personalized anomalous facial expression detection. In: VISIGRAPP (5: VISAPP), pp. 274–283 (2018)

    Google Scholar 

  11. Ge, L., Gao, J., Ngo, H., Li, K., Zhang, A.: On handling negative transfer and imbalanced distributions in multiple source transfer learning. Stat. Anal. Data Mining ASA Data Sci. J. 7(4), 254–271 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  12. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)

    Google Scholar 

  13. Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. In: Proceedings of the Web Conference 2020, pp. 2704–2710 (2020)

    Google Scholar 

  14. Idé, T., Phan, D.T., Kalagnanam, J.: Multi-task multi-modal models for collective anomaly detection. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 177–186. IEEE (2017)

    Google Scholar 

  15. Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)

  16. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)

    Google Scholar 

  17. Kumagai, A., Iwata, T., Fujiwara, Y.: Transfer anomaly detection by inferring latent domain representations. In: Advances in Neural Information Processing Systems, pp. 2471–2481 (2019)

    Google Scholar 

  18. Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE (2008)

    Google Scholar 

  19. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural. Inf. Process. Syst. 26, 3111–3119 (2013)

    Google Scholar 

  20. Park, C., Kim, D., Zhu, Q., Han, J., Yu, H.: Task-guided pair embedding in heterogeneous network. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 489–498 (2019)

    Google Scholar 

  21. Patcha, A., Park, J.M.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)

    Article  Google Scholar 

  22. Robertson, W.K., Maggi, F., Kruegel, C., Vigna, G., et al.: Effective anomaly detection with scarce training data. In: NDSS. Citeseer (2010)

    Google Scholar 

  23. Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. arXiv preprint arXiv:2106.08265 (2021)

  24. Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38

    Chapter  Google Scholar 

  25. Tang, J., Qu, M., Mei, Q.: PTE: predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165–1174 (2015)

    Google Scholar 

  26. Teng, X., Yan, M., Ertugrul, A.M., Lin, Y.R.: Deep into hypersphere: robust and unsupervised anomaly discovery in dynamic networks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (2018)

    Google Scholar 

  27. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  28. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=rJXMpikCZ

  29. Wang, X., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference, pp. 2022–2032 (2019)

    Google Scholar 

  30. Yamaguchi, M., Koizumi, Y., Harada, N.: AdaFlow: domain-adaptive density estimator with application to anomaly detection and unpaired cross-domain translation. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3647–3651. IEEE (2019)

    Google Scholar 

  31. Yang, Z., Bozchalooi, I.S., Darve, E.: Anomaly detection with domain adaptation. arXiv preprint arXiv:2006.03689 (2020)

  32. Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 793–803 (2019)

    Google Scholar 

Download references

Acknowledgment

This work was partially supported by NSFC under Grant No. 62272008 and 61832001.

Author information

Authors and Affiliations

  1. The Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, China

    Li Zheng & Jun Gao

  2. School of Computer Science, Peking University, Beijing, China

    Li Zheng & Jun Gao

  3. Zhejiang University, Hangzhou, China

    Zhao Li

  4. Hangzhou Link2Do Technology, Hangzhou, China

    Zhao Li

  5. Alibaba Group, Hangzhou, China

    Zhenpeng Li

  6. School of Computing, Macquarie University, Sydney, Australia

    Jia Wu

  7. Academy of Mathematics and System Science, Chinese Academy of Sciences, Beijing, China

    Chuan Zhou

Authors
  1. Li Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenpeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jia Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chuan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhao Li or Jun Gao .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zheng, L., Li, Z., Gao, J., Li, Z., Wu, J., Zhou, C. (2023). Domain Adaptation for Anomaly Detection on Heterogeneous Graphs in E-Commerce. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981. Springer, Cham. https://doi.org/10.1007/978-3-031-28238-6_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28238-6_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28237-9

  • Online ISBN: 978-3-031-28238-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation